{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:4DHPLJOWJ6XH2CC2I6E3VHAWUK","short_pith_number":"pith:4DHPLJOW","schema_version":"1.0","canonical_sha256":"e0cef5a5d64fae7d085a4789ba9c16a2ad8d000addd7aaad56881db061bcfb12","source":{"kind":"arxiv","id":"2206.12634","version":1},"attestation_state":"computed","paper":{"title":"SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Congcong Li, Dexiang Hong, Longyin Wen, Xiaoqi Ma, Xinyao Wang, Yufei Wang","submitted_at":"2022-06-25T12:27:13Z","abstract_excerpt":"This report presents the algorithm used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR 2022. In this work, we improve the existing Structured Context Transformer (SC-Transformer) method for GEBD. Specifically, a transformer decoder module is added after transformer encoders to extract high quality frame features. The final classification is performed jointly on the results of the original binary classifier and a newly introduced multi-class classifier branch. To enrich motion information, optical flow is introduced as a new modality. Finally, model ensemble is u"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2206.12634","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-06-25T12:27:13Z","cross_cats_sorted":[],"title_canon_sha256":"54e7ec6e76d81171d0be60013612f2f87404c9a2cc0eac4844280ca7d258d6d9","abstract_canon_sha256":"1e5149ac1d6367603f07d0b3ae93c074dd96d7bbdb8333489b44a7c212ce3e7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:34:57.285304Z","signature_b64":"YW6WhzNhneE9BUalTRu+O+PCWI2fZjojTvFC7TPdnICXg4CmWonGN5yMptSLgmr0TFUKJ1dRodlwjPCwut91AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0cef5a5d64fae7d085a4789ba9c16a2ad8d000addd7aaad56881db061bcfb12","last_reissued_at":"2026-07-05T04:34:57.284965Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:34:57.284965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Congcong Li, Dexiang Hong, Longyin Wen, Xiaoqi Ma, Xinyao Wang, Yufei Wang","submitted_at":"2022-06-25T12:27:13Z","abstract_excerpt":"This report presents the algorithm used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR 2022. In this work, we improve the existing Structured Context Transformer (SC-Transformer) method for GEBD. Specifically, a transformer decoder module is added after transformer encoders to extract high quality frame features. The final classification is performed jointly on the results of the original binary classifier and a newly introduced multi-class classifier branch. To enrich motion information, optical flow is introduced as a new modality. Finally, model ensemble is u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.12634","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2206.12634/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2206.12634","created_at":"2026-07-05T04:34:57.285035+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.12634v1","created_at":"2026-07-05T04:34:57.285035+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.12634","created_at":"2026-07-05T04:34:57.285035+00:00"},{"alias_kind":"pith_short_12","alias_value":"4DHPLJOWJ6XH","created_at":"2026-07-05T04:34:57.285035+00:00"},{"alias_kind":"pith_short_16","alias_value":"4DHPLJOWJ6XH2CC2","created_at":"2026-07-05T04:34:57.285035+00:00"},{"alias_kind":"pith_short_8","alias_value":"4DHPLJOW","created_at":"2026-07-05T04:34:57.285035+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4DHPLJOWJ6XH2CC2I6E3VHAWUK","json":"https://pith.science/pith/4DHPLJOWJ6XH2CC2I6E3VHAWUK.json","graph_json":"https://pith.science/api/pith-number/4DHPLJOWJ6XH2CC2I6E3VHAWUK/graph.json","events_json":"https://pith.science/api/pith-number/4DHPLJOWJ6XH2CC2I6E3VHAWUK/events.json","paper":"https://pith.science/paper/4DHPLJOW"},"agent_actions":{"view_html":"https://pith.science/pith/4DHPLJOWJ6XH2CC2I6E3VHAWUK","download_json":"https://pith.science/pith/4DHPLJOWJ6XH2CC2I6E3VHAWUK.json","view_paper":"https://pith.science/paper/4DHPLJOW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.12634&json=true","fetch_graph":"https://pith.science/api/pith-number/4DHPLJOWJ6XH2CC2I6E3VHAWUK/graph.json","fetch_events":"https://pith.science/api/pith-number/4DHPLJOWJ6XH2CC2I6E3VHAWUK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4DHPLJOWJ6XH2CC2I6E3VHAWUK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4DHPLJOWJ6XH2CC2I6E3VHAWUK/action/storage_attestation","attest_author":"https://pith.science/pith/4DHPLJOWJ6XH2CC2I6E3VHAWUK/action/author_attestation","sign_citation":"https://pith.science/pith/4DHPLJOWJ6XH2CC2I6E3VHAWUK/action/citation_signature","submit_replication":"https://pith.science/pith/4DHPLJOWJ6XH2CC2I6E3VHAWUK/action/replication_record"}},"created_at":"2026-07-05T04:34:57.285035+00:00","updated_at":"2026-07-05T04:34:57.285035+00:00"}